Kaisa_2012_3_photo by Veikko Somerpuro

Ilmoittaudu
20.12.2017 klo 15:00 - 28.2.2018 klo 23:59
Moodle
Kirjaudu sisään nähdäksesi Moodlen kurssiavaimen.

Aikataulu

Tästä osiosta löydät kurssin opetusaikataulun. Tarkista mahdolliset muut aikataulut kuvauksesta.

PäivämääräAikaOpetuspaikka
Ti 16.1.2018
14:15 - 16:00
To 18.1.2018
14:15 - 16:00
Ti 23.1.2018
14:15 - 16:00
To 25.1.2018
14:15 - 16:00
Ti 30.1.2018
14:15 - 16:00
To 1.2.2018
14:15 - 16:00
Ti 6.2.2018
14:15 - 16:00
To 8.2.2018
14:15 - 16:00
Ti 13.2.2018
14:15 - 16:00
To 15.2.2018
14:15 - 16:00
Ti 20.2.2018
14:15 - 16:00
To 22.2.2018
14:15 - 16:00
Ti 27.2.2018
14:15 - 16:00
To 1.3.2018
14:15 - 16:00
To 8.3.2018
14:15 - 17:00

Kuvaus

Optional course.
Master's Programme in Mathematics and Statistics is responsible for the course.
The course belongs to the Statistics and Social statistics module.
The course is available to students from other degree programmes.

BSc courses on linear algebra, probability calculus, statistical inference

Introduction to Machine Learning (A significant portion of Differential Privacy literature deals with machine learning and statistical inference)

A basic grasp of Differential Privacy (a mathematical framework with strong privacy guarantees), its variants, and techniques that are used as building blocks for differentially private methods

Recommended time/stage of studies for completion: 1. or 2. year
Term/teaching period when the course will be offered: Spring term, 3rd or 4th period

Need for Privacy: Threats against identities of “anonymized” data providers and famous debacles from history, privacy-preserving data analysis, Differential Privacy (DP), privacy-enabling mechanisms (Laplace, Gaussian, Exponential mechanisms), relaxations (approximate, Rényi, concentrated DP), composition theorems

Exam and student presentations

Lecture notes and articles to be decided in class

Lectures, short student presentations to summarize important articles from literature

Exam and student presentations, Course will be graded with grades 1-5

Exam and student presentations